{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.8.1-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python38164bitec4538a0ed7a4029b9bd19594323cc7e",
   "display_name": "Python 3.8.1 64-bit"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"./titanic/train.csv\")\n",
    "test = pd.read_csv(\"./titanic/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count          891\nunique         681\ntop       CA. 2343\nfreq             7\nName: Ticket, dtype: object"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Ticket.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<bound method Series.unique of 0             A/5 21171\n1              PC 17599\n2      STON/O2. 3101282\n3                113803\n4                373450\n             ...       \n886              211536\n887              112053\n888          W./C. 6607\n889              111369\n890              370376\nName: Ticket, Length: 891, dtype: object>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Ticket.unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>714.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>446.000000</td>\n      <td>0.383838</td>\n      <td>2.308642</td>\n      <td>29.699118</td>\n      <td>0.523008</td>\n      <td>0.381594</td>\n      <td>32.204208</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>257.353842</td>\n      <td>0.486592</td>\n      <td>0.836071</td>\n      <td>14.526497</td>\n      <td>1.102743</td>\n      <td>0.806057</td>\n      <td>49.693429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.420000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>223.500000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>20.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.910400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>446.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14.454200</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>668.500000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>38.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>31.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>891.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>80.000000</td>\n      <td>8.000000</td>\n      <td>6.000000</td>\n      <td>512.329200</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  "
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 1440x1080 with 9 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "train.hist(figsize=(20,15), bins = 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<bound method NDFrame.describe of 0      3\n1      1\n2      3\n3      1\n4      3\n      ..\n886    2\n887    1\n888    3\n889    1\n890    3\nName: Pclass, Length: 891, dtype: int64>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Pclass.describe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop(['Survived'],axis=1)\n",
    "y = train.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   PassengerId  Pclass                                               Name  \\\n0            1       3                            Braund, Mr. Owen Harris   \n1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n2            3       3                             Heikkinen, Miss. Laina   \n3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n4            5       3                           Allen, Mr. William Henry   \n\n      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n3  female  35.0      1      0            113803  53.1000  C123        S  \n4    male  35.0      0      0            373450   8.0500   NaN        S  "
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<bound method NDFrame.describe of 0       NaN\n1       C85\n2       NaN\n3      C123\n4       NaN\n       ... \n886     NaN\n887     B42\n888     NaN\n889    C148\n890     NaN\nName: Cabin, Length: 891, dtype: object>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.Cabin.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n       'C148'], dtype=object)"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.Cabin.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "['110152',\n '110413',\n '110465',\n '110564',\n '110813',\n '111240',\n '111320',\n '111361',\n '111369',\n '111426',\n '111427',\n '111428',\n '112050',\n '112052',\n '112053',\n '112058',\n '112059',\n '112277',\n '112379',\n '113028',\n '113043',\n '113050',\n '113051',\n '113055',\n '113056',\n '113059',\n '113501',\n '113503',\n '113505',\n '113509',\n '113510',\n '113514',\n '113572',\n '113760',\n '113767',\n '113773',\n '113776',\n '113781',\n '113783',\n '113784',\n '113786',\n '113787',\n '113788',\n '113789',\n '113792',\n '113794',\n '113796',\n '113798',\n '113800',\n '113803',\n '113804',\n '113806',\n '113807',\n '11668',\n '11751',\n '11752',\n '11753',\n '11755',\n '11765',\n '11767',\n '11769',\n '11771',\n '11774',\n '11813',\n '11967',\n '12233',\n '12460',\n '12749',\n '13049',\n '13213',\n '13214',\n '13502',\n '13507',\n '13509',\n '13567',\n '13568',\n '14311',\n '14312',\n '14313',\n '14973',\n '1601',\n '16966',\n '16988',\n '17421',\n 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3101312',\n 'SOTON/O.Q. 392078',\n 'SOTON/O.Q. 392087',\n 'SOTON/O2 3101272',\n 'SOTON/O2 3101287',\n 'SOTON/OQ 3101316',\n 'SOTON/OQ 3101317',\n 'SOTON/OQ 392076',\n 'SOTON/OQ 392082',\n 'SOTON/OQ 392086',\n 'SOTON/OQ 392089',\n 'SOTON/OQ 392090',\n 'STON/O 2. 3101269',\n 'STON/O 2. 3101273',\n 'STON/O 2. 3101274',\n 'STON/O 2. 3101275',\n 'STON/O 2. 3101280',\n 'STON/O 2. 3101285',\n 'STON/O 2. 3101286',\n 'STON/O 2. 3101288',\n 'STON/O 2. 3101289',\n 'STON/O 2. 3101292',\n 'STON/O 2. 3101293',\n 'STON/O 2. 3101294',\n 'STON/O2. 3101271',\n 'STON/O2. 3101279',\n 'STON/O2. 3101282',\n 'STON/O2. 3101283',\n 'STON/O2. 3101290',\n 'SW/PP 751',\n 'W./C. 14258',\n 'W./C. 14263',\n 'W./C. 6607',\n 'W./C. 6608',\n 'W./C. 6609',\n 'W.E.P. 5734',\n 'W/C 14208',\n 'WE/P 5735']"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(X.Ticket.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name, Cabin, Ticket is not neccessary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n      dtype='object')"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attr_names):\n",
    "        self.attr_name = attr_names\n",
    "    def fit (self, X, y = None):\n",
    "        return self \n",
    "    def transform(self, X):\n",
    "        return X[self.attr_name].values\n",
    "class DataFrameRemover(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attr_names):\n",
    "        self.attr_name = attr_names\n",
    "    def fit (self, X, y = None):\n",
    "        return self \n",
    "    def transform(self, X):\n",
    "        return X.drop(self.attr_name, axis = 1)\n",
    "class MyLabelBinarizer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        self.encode = LabelBinarizer()\n",
    "    def fit (self, X, y = None):\n",
    "        return self.encode.fit(X) \n",
    "    def transform(self, X):\n",
    "        return self.encode.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "discret_pipe = Pipeline((\n",
    "    ('selector', DataFrameSelector([ 'Embarked'])),\n",
    "    ('imputer',SimpleImputer(strategy=\"most_frequent\")),\n",
    "    ('binarize', MyLabelBinarizer())\n",
    "))\n",
    "\n",
    "num_pipe = Pipeline((\n",
    "    ('selector', DataFrameSelector(\n",
    "        ['Age', 'SibSp', 'Parch','Fare']\n",
    "    )),\n",
    "    ('dummy_data', SimpleImputer(strategy=\"median\"))\n",
    "))\n",
    "\n",
    "gender_pipe = Pipeline((\n",
    "    ('selector', DataFrameSelector(['Sex'])),\n",
    "    ('binarize', MyLabelBinarizer())\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0, 0, 1],\n       [1, 0, 0],\n       [0, 0, 1],\n       ...,\n       [0, 0, 1],\n       [1, 0, 0],\n       [0, 1, 0]])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discret_pipe.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1],\n       [0],\n       [0],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [0],\n       [0],\n       [0],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [0],\n       [0],\n       [0],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n     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  [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [0],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [1],\n       [1],\n       [1],\n       [1],\n       [1],\n       [1],\n     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  [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [0],\n       [0],\n       [0],\n       [0],\n       [1],\n       [0],\n       [1],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [1],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1],\n       [1],\n       [0],\n       [0],\n       [1],\n       [0],\n       [1],\n       [1],\n       [0],\n       [1],\n       [0],\n       [0],\n       [1],\n       [1]])"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_pipe.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[22.    ,  1.    ,  0.    ,  7.25  ],\n       [38.    ,  1.    ,  0.    , 71.2833],\n       [26.    ,  0.    ,  0.    ,  7.925 ],\n       ...,\n       [28.    ,  1.    ,  2.    , 23.45  ],\n       [26.    ,  0.    ,  0.    , 30.    ],\n       [32.    ,  0.    ,  0.    ,  7.75  ]])"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipe.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_pipe = FeatureUnion((\n",
    "    ('nums',num_pipe),\n",
    "    ('embarked',discret_pipe),\n",
    "    ('gender', gender_pipe)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[22.,  1.,  0., ...,  0.,  1.,  1.],\n       [38.,  1.,  0., ...,  0.,  0.,  0.],\n       [26.,  0.,  0., ...,  0.,  1.,  0.],\n       ...,\n       [28.,  1.,  2., ...,  0.,  1.,  0.],\n       [26.,  0.,  0., ...,  0.,  0.,  1.],\n       [32.,  0.,  0., ...,  1.,  0.,  1.]])"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_pipe.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.64804469, 0.65168539, 0.70786517, 0.73595506, 0.70786517])"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "cross_val_score(\n",
    "    KNeighborsClassifier(),\n",
    "    final_pipe.fit_transform(X), y,\n",
    "    scoring='accuracy',n_jobs= -1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.78212291, 0.79775281, 0.76966292, 0.76404494, 0.81460674])"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "cross_val_score(\n",
    "    LogisticRegression(),\n",
    "    final_pipe.fit_transform(X), y,\n",
    "    scoring='accuracy',n_jobs= -1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.77653631, 0.79213483, 0.8258427 , 0.75842697, 0.80337079])"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "cross_val_score(\n",
    "    RandomForestClassifier(),\n",
    "    final_pipe.fit_transform(X), y,\n",
    "    scoring='accuracy',n_jobs= -1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = LogisticRegression()\\\n",
    "    .fit(final_pipe.fit_transform(X), y)\\\n",
    "    .predict(final_pipe.fit_transform(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 892,    0],\n       [ 893,    1],\n       [ 894,    0],\n       [ 895,    0],\n       [ 896,    1],\n       [ 897,    0],\n       [ 898,    1],\n       [ 899,    0],\n       [ 900,    1],\n       [ 901,    0],\n       [ 902,    0],\n       [ 903,    0],\n       [ 904,    1],\n       [ 905,    0],\n       [ 906,    1],\n       [ 907,    1],\n       [ 908,    0],\n       [ 909,    0],\n       [ 910,    1],\n       [ 911,    1],\n       [ 912,    0],\n       [ 913,    0],\n       [ 914,    1],\n       [ 915,    0],\n       [ 916,    1],\n       [ 917,    0],\n       [ 918,    1],\n       [ 919,    0],\n       [ 920,    0],\n       [ 921,    0],\n       [ 922,    0],\n       [ 923,    0],\n       [ 924,    1],\n       [ 925,    1],\n       [ 926,    0],\n       [ 927,    0],\n       [ 928,    1],\n       [ 929,    1],\n       [ 930,    0],\n       [ 931,    0],\n       [ 932,    0],\n       [ 933,    0],\n       [ 934,    0],\n       [ 935,    1],\n       [ 936,    1],\n       [ 937,    0],\n       [ 938,    0],\n       [ 939,    0],\n       [ 940,    1],\n       [ 941,    1],\n       [ 942,    0],\n       [ 943,    0],\n       [ 944,    1],\n       [ 945,    1],\n       [ 946,    0],\n       [ 947,    0],\n       [ 948,    0],\n       [ 949,    0],\n       [ 950,    0],\n       [ 951,    1],\n       [ 952,    0],\n       [ 953,    0],\n       [ 954,    0],\n       [ 955,    1],\n       [ 956,    1],\n       [ 957,    1],\n       [ 958,    1],\n       [ 959,    0],\n       [ 960,    0],\n       [ 961,    1],\n       [ 962,    1],\n       [ 963,    0],\n       [ 964,    1],\n       [ 965,    0],\n       [ 966,    1],\n       [ 967,    1],\n       [ 968,    0],\n       [ 969,    0],\n       [ 970,    0],\n       [ 971,    1],\n       [ 972,    0],\n       [ 973,    1],\n       [ 974,    0],\n       [ 975,    0],\n       [ 976,    0],\n       [ 977,    0],\n       [ 978,    1],\n       [ 979,    1],\n       [ 980,    1],\n       [ 981,    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0],\n       [1164,    1],\n       [1165,    1],\n       [1166,    0],\n       [1167,    1],\n       [1168,    0],\n       [1169,    0],\n       [1170,    0],\n       [1171,    0],\n       [1172,    1],\n       [1173,    0],\n       [1174,    1],\n       [1175,    1],\n       [1176,    1],\n       [1177,    0],\n       [1178,    0],\n       [1179,    0],\n       [1180,    0],\n       [1181,    0],\n       [1182,    0],\n       [1183,    1],\n       [1184,    0],\n       [1185,    0],\n       [1186,    0],\n       [1187,    0],\n       [1188,    1],\n       [1189,    0],\n       [1190,    0],\n       [1191,    0],\n       [1192,    0],\n       [1193,    0],\n       [1194,    0],\n       [1195,    0],\n       [1196,    1],\n       [1197,    0],\n       [1198,    0],\n       [1199,    0],\n       [1200,    0],\n       [1201,    1],\n       [1202,    0],\n       [1203,    0],\n       [1204,    0],\n       [1205,    1],\n       [1206,    1],\n       [1207,    1],\n       [1208,    1],\n       [1209,    0],\n       [1210,    0],\n       [1211,    0],\n       [1212,    0],\n       [1213,    0],\n       [1214,    0],\n       [1215,    0],\n       [1216,    1],\n       [1217,    0],\n       [1218,    1],\n       [1219,    0],\n       [1220,    0],\n       [1221,    0],\n       [1222,    1],\n       [1223,    0],\n       [1224,    0],\n       [1225,    1],\n       [1226,    0],\n       [1227,    0],\n       [1228,    0],\n       [1229,    0],\n       [1230,    0],\n       [1231,    0],\n       [1232,    0],\n       [1233,    0],\n       [1234,    0],\n       [1235,    1],\n       [1236,    0],\n       [1237,    1],\n       [1238,    0],\n       [1239,    1],\n       [1240,    0],\n       [1241,    1],\n       [1242,    1],\n       [1243,    0],\n       [1244,    0],\n       [1245,    0],\n       [1246,    1],\n       [1247,    0],\n       [1248,    1],\n       [1249,    0],\n       [1250,    0],\n       [1251,    1],\n       [1252,    0],\n       [1253,    1],\n       [1254,    1],\n       [1255,    0],\n       [1256,    1],\n       [1257,    0],\n       [1258,    0],\n       [1259,    1],\n       [1260,    1],\n       [1261,    0],\n       [1262,    0],\n       [1263,    1],\n       [1264,    0],\n       [1265,    0],\n       [1266,    1],\n       [1267,    1],\n       [1268,    1],\n       [1269,    0],\n       [1270,    0],\n       [1271,    0],\n       [1272,    0],\n       [1273,    0],\n       [1274,    1],\n       [1275,    1],\n       [1276,    0],\n       [1277,    1],\n       [1278,    0],\n       [1279,    0],\n       [1280,    0],\n       [1281,    0],\n       [1282,    0],\n       [1283,    1],\n       [1284,    0],\n       [1285,    0],\n       [1286,    0],\n       [1287,    1],\n       [1288,    0],\n       [1289,    1],\n       [1290,    0],\n       [1291,    0],\n       [1292,    1],\n       [1293,    0],\n       [1294,    1],\n       [1295,    0],\n       [1296,    0],\n       [1297,    0],\n       [1298,    0],\n       [1299,    1],\n       [1300,    1],\n       [1301,    1],\n       [1302,    1],\n       [1303,    1],\n       [1304,    1],\n       [1305,    0],\n       [1306,    1],\n       [1307,    0],\n       [1308,    0],\n       [1309,    0]])"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack([test.PassengerId.values.reshape((-1,1)), y_pred.reshape((-1,1))]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n    </tr>\n    <tr>\n      <th>PassengerId</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>892</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>893</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>894</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>895</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>896</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1305</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1306</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1307</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1308</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1309</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>418 rows × 1 columns</p>\n</div>",
      "text/plain": "             Survived\nPassengerId          \n892                 0\n893                 1\n894                 0\n895                 0\n896                 1\n...               ...\n1305                0\n1306                1\n1307                0\n1308                0\n1309                0\n\n[418 rows x 1 columns]"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(\n",
    "    np.hstack([test.PassengerId.values.reshape((-1,1)), y_pred.reshape((-1,1))]) ,\n",
    "    columns = ['PassengerId','Survived']\n",
    ")\n",
    "df.index = df.PassengerId\n",
    "df = df.drop(['PassengerId'],axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./mypred.csv')"
   ]
  }
 ]
}